Getaw Tadasse, Bernadina Algieri, Matthias Kalkuhl, and Joachim von Braun
3.5 Results and Discussion
3.5.3 Food Price Trigger
Recent discussions about food prices noted the possibility of a tipping point where the market may stop responding “normally” to market changes, opting instead to exaggerate and overreact. In order to identify triggers and test the tipping-point hypothesis, we estimated a series of quantile regressions for both the price spike and the volatility equations. The quantile regressions indicate the price or volatility levels at which the dynamics of price spikes and price volatility change (or whether the dynamics estimated in Tables3.1and3.3are robust for all price and volatility levels). In the price spike equation, the effects of oil prices, speculative futures trading, and supply shocks are compared at both higher and lower prices. In the volatility equation, the effects of supply shocks, oil price volatility, and the financial crisis index are compared at both lower and higher volatility. The tips in the price spike and price volatility equation are therefore different. In the price spike equation, the upper tip denotes the highest price, but in the price volatility equation, a high quantile signifies high volatility.
The results are presented in Figs.3.3and3.4. The figures show the marginal effects of the explanatory variables on the response variables at different level of quantiles. The line graphs indicate point estimates, and the shaded regions
−.20.2.4Coefficient
Fig. 3.3 Triggers of food price spikes.Source: Authors’ estimation based on data explained in Sects.3.3and3.4.Note: Themiddle lineshows the coefficient which explains price spikes using (a) oil price shocks, (b) production shocks, (c) excessive speculation, and (d) stock-to-use ratios.
The quantile regression shows the coefficients for different quantiles of commodity price spikes.
At low quantiles, the corresponding coefficient shows the impact on price spikes when price spikes are low; at high quantiles, the corresponding coefficient shows the impact on price spikes when price spikes are already high. Shaded regions are the 95 % confidence intervals, and thelinein the middleis the coefficient
−2−101Coefficient
0 .2 .4 .6 .8 1
Food prodction shock
−.0010.001.002Coefficient
0 .2 .4 .6 .8 1
Finacial crisis index
0.2.4.6.81Coefficient
0 .2 .4 .6 .8 1
Quantile Oil price volatility
−.00050.0005Coefficient
0 .2 .4 .6 .8 1
Exssesive speculation activity
−.50.511.5Coefficient
0 .2 .4 .6 .8 1
Beginning stock−to−use ratio
−.020.02.04.06Coefficient
0 .2 .4 .6 .8 1
Quantile GDP growth rate
Fig. 3.4 Triggers of global food price volatility. Source: Authors’ estimation based on data explained in Sects.3.3and3.4.Note: Themiddle lineshows the coefficient which explains food price volatility using different explanatory variables. The quantile regression shows the coefficients for different quantiles of food price volatility. At low quantiles, the corresponding coefficient shows the impact on price volatility when volatility is low; at high quantiles, the corresponding coefficient shows the impact on price volatility when volatility is high.Shaded regionsare the 95 % confidence intervals, and thelinein themiddleis the coefficient
show the 95 % confidence intervals. A variable is defined as a trigger if the confidence intervals do not include zero values in the shaded region and if the line graph is visibly increasing (a positive relationship between food price and variable) or decreasing (a negative relationship between food price and the variable) as the quantile increases. The results of triggering price spikes are mixed. Of all the variables included in the price spike equation (Fig.3.3), the trigger effect is evident only when maize or wheat production experiences a shock, or when there is speculation on maize. Other variables such as oil prices and stock-to-use ratio
have no trigger effects, as depicted by flat and insignificant marginal values over quantiles.
The effect of production shocks on price spikes generally becomes stronger as the quantile increases, except in the case of soybeans. This result could imply that the USDA production forecasts have a larger impact on price movements when prices are high rather than low. Thus, production shocks are a significant contributor to food price spikes.
The u-shaped curve visible in the quantile regressions for speculation sug-gests that speculation is more important in times of extreme price dynamics. An increasing price trend, driven by changes in fundamentals (commodity demand and supply), gives rise to market nervousness, causing speculators to overheat the market. Speculation is also observed to have a strong impact on price spikes at lower quantiles of price spikes. This is an indication of the stabilizing effect of speculation when markets are calm. When markets are flooded, since the lower spike quantiles are negative values, an increase in speculative activities restores market prices. In sum, speculation has the capacity to create price hikes and reduce price slumps.
The results from the volatility quantile regression suggest the importance of oil prices in triggering food price volatility (Fig.3.4). The effects of supply shocks, stock-to-use ratio, and global GDP growth also increase over quantiles, but they are all statistically insignificant. The evidence also shows that financial crises and speculation do not necessarily trigger volatility, in contrast to price spikes as shown in the quantile analysis above.
Oil prices have remained a primary factor in causing extreme volatility in food prices. Apart from being affected by production costs and biofuel-related demand, food price volatility is also affected by oil prices through a real income effect. This is because of oil prices’ dominant impact on the overall economy. The trigger effect may be associated with the interaction between these effects. All the effects are evident at the higher level of food prices.
3.6 Conclusion
This study has investigated the main drivers of food price spikes and volatility for wheat, maize, and soybeans. It has also shown how these factors trigger a crisis when there are extreme price changes. The analysis has indicated that exogenous shocks as well as the linkages between food, energy, and financial markets play a significant role in explaining food price volatility and price spikes.
In addition to demand and supply shocks, speculation is an important factor in explaining and triggering extreme price spikes. Excessive speculation is more strongly associated with price spikes at extreme positive price changes rather than negative price changes. This implies that the stabilizing effect of speculation (generated through price discovery) is smaller than its destabilizing effect (generated through creating market bubbles).
The results also confirm that supply shocks are reflected in price spikes and that oil price shocks affect price risk more than they affect food crises. The effect of oil
prices on food price spikes has become significant only in recent years. Financial crisis exerts a strong impact on food price volatility, which confirms that the link between financial and commodity markets is becoming stronger.
On the basis of the empirical results, it seems opportune for policymakers to prevent excessive speculative behaviors in the commodity market in order to reduce price spikes and prevent short-term food crises. In this context, policymakers could put caps on trading in extreme market situations or impose a tax on food commodity futures trading, along the lines of the Tobin tax. Designing flexible biofuel policies that are responsive to the food supply situation can also help stabilize prices and reduce volatility spillovers from oil markets in times of a food crisis. Recent changes in the US biofuel mandate, for example, include flexibility mechanisms that allow for relaxing the blending requirement in a certain year if compensated for in another year.
Improving the market information base would further help all market actors to form their expectations based on fundamentals and to detect shortages early. While the Agricultural Market Information System (AMIS), an initiative of the G20, strives for higher transparency, contributions from some of the member states are still insufficient.
Recently, many countries are increasing their national grain stocks to reduce domestic volatility and import dependency, leading to an increased grain scarcity and in turn higher grain prices in the short term. International levels of storage, however, are only one of the options to reduce volatility, and they turned out to be mostly insignificant in our analyses. One reason might be the lack of cooperation between countries: The governments which build stocks only for their citizens tend to complement storage policies with trade restrictions, effectively withdrawing their stocks from the global grain market. Such failure to act collectively needs to be addressed in regional and global trade talks. The international consequences of national stock-holding policies should also be discussed during these talks.
Besides policies to reduce volatility and prevent extreme price spikes, govern-ments can improve the resilience of producers and consumers to price changes. This can be achieved by supporting contract farming and price insurance mechanisms on the production side and by enhancing safety nets and access to financial services on the consumer side.
Governments and their international associations such as the G20 should there-fore carefully analyze all available options for preventing food price spikes and volatility—from interventions in financial markets to biofuel policies—and they should also facilitate market information.
Acknowledgment The authors acknowledge financial support from the European Commission (FoodSecure Research Project) and the Federal Ministry of Economic Cooperation and Develop-ment of Germany (Research Project on Commodity Price Volatility, Trade Policy and the Poor).
Open Access This chapter is distributed under the terms of the Creative Commons Attribution-Noncommercial 2.5 License (http://creativecommons.org/licenses/by-nc/2.5/) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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